Group Number: 10

Names: Chan, C., De Jose, B., Esquillo, V., & Pasia, A.

Final Project: Storytelling with Data

How does the Philippines Compare with fellow ASEAN Nations?

Notebook Primer

Rationale

News headlines often articulate the shortcomings or incompetence of the Philippines in different metrics. But is that really the case? Do Filipinos really have it bad at present? What about before?

It is unfair to compare the Philippines to super power countries in answering this question, and it is for this reason that the study group chose its fellow ASEAN Nations -- Philippines' most immediate neighbors -- as the baseline for comparison.

Areas of Comparison

The areas of comparison for Philippines, and to an effect, each ASEAN Nation, will be: Economic Fitness, Environment, Social, Governance, and their Overall COVID-19 Response

Datasets

The study group will be utilizing four datasets, 2 of which came from World Bank, 1 from WHO, and the last one from Oxford. These are briefly defined below

  1. Economic Fitness Dataset

    is the measure of a country’s diversification and ability to produce complex goods on a globally competitive basis

  2. Environmental, Social, and Governance Dataset

    has 67 indicators for Environmental, Social and Governance aspects

    For the indicators, the study group chose the following indicators

    Environmental:

    Natural resource depletion is the sum of net forest depletion, energy depletion, and mineral depletion. Net forest depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime. It covers coal, crude oil, and natural gas. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.

    Social:

    Voice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

    Governance:

    Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

  3. WHO COVID-19 Global Dataset

    Contains the global data regarding COVID-19

  4. Oxford COVID-19 Government Response Tracker

    The Oxford Blavatnik School of Government defines this dataset as

    Governments are taking a wide range of measures to tackle the COVID-19 outbreak. We aim to track and compare worldwide government responses to the coronavirus rigorously and consistently. Systematic information on which measures governments take, and when, can help us understand the responses in a consistent way, aiding efforts to fight the pandemic.

    Our team collects information on common policy responses, scores the stringency of such measures, and aggregates these into a Stringency Index.

Note: All latest data used in the analyses were held only up until December 31, 2020.

Notebook Flow

The study group followed a modular approach as to make it easier to manage the program. Cleaning and processing the datasets required a case-to-case approach, thus, as seen below, each cleaning and processing task was implemented using a function specific to each dataset. In this regard, comments were provided in each function to aid the reader.

With regards to the general flow of the notebook, the block diagram below summarizes the process

Blank diagram (2).png

Brief insights/observations are interjected after every visualization. Detailed discussions are found after.

Package Dependencies

  1. Pandas
  2. OpenPyxl
  3. Numpy
  4. Matplotlib

Load Packages and Data

The function info_prime(df) will be used to gain initial insights into the structure of each dataset.

Data Processing Proper

Economic Fitness Dataset

Initialize Data Processing Functions

Note

If the countries extracted contained all 10 ASEAN Nations, the processing was a success. These countries were then instantiated as separate datasets from each other, which were then stored into a list. This approach was taken as the notebook revolved around list comprehensions to make our lives easier.

ESG Dataset

Initialize Data Processing Functions

As you can see, the indicators of interest were not immediately 'sliced' out. This enabled backtracking much easier should we have changed our minds in the middle of our analysis.

Note

If the countries extracted contained all 10 ASEAN Nations, the processing was a success. These countries were then instantiated as separate datasets from each other, which were then stored into a list. This approach was taken as the notebook revolved around list comprehensions to make our lives easier.

COVID-19 Datasets

WHO COVID-19 Cases

Initialize Data Processing Functions

The data processing function for the WHO COVID-19 Dataset included a normalization code block to normalize each feature. This was conducted to sort of 'scale' each variable such that it will be easier to compare. Furthermore, in case you have not noticed from the past processing functions, the attribute df.name was always created for each dataset so as to make it easier to distinguish which dataframe is which from the returned df_list. The name attribute was also used as a container to make it easier to name titles, and x- or y-labels when it comes to data visualization; this aided in the cumbersome process of manually naming each of the 10 countries for a single plot

Note

If the countries extracted contained all 10 ASEAN Nations, the processing was a success. These countries were then instantiated as separate datasets from each other, which were then stored into a list. This approach was taken as the notebook revolved around list comprehensions to make our lives easier.

Government Response Index

Initialize Data Processing Functions

Nothing special here. This is the usual data processing code block as shown in the block diagram in the notebook primer above.

Note

If the countries extracted contained all 10 ASEAN Nations, the processing was a success. These countries were then instantiated as separate datasets from each other, which were then stored into a list. This approach was taken as the notebook revolved around list comprehensions to make our lives easier.

Results / Data Visualization Proper

Load Packages

Initialize Plotting Functions

In generating a boxplot, the study group used the df.boxplot approach instead of using matplotlib. This is because the pandas method made it easier to deal with NaNs. Using the pandas method however, does not enable us to make use of the dictionary that is returned using matplotlib as containers for the legend labels. Thus in the gen_boxplot(df_list, column) function, it can be seen that a proxy_artist was used instead to create legends.

Descriptive Statistics and Boxplots

For each dataset or indicator, the descriptive statistics and a boxpot is shown. The descriptive statistics is sorted by the mean, from highest to lowest, to make it easier to compare each ASEAN Nation with their performance for each metric

Economic Fitness

Economic Fitness Insights

As observed, Philippines rank somewhere in the middle. Brunei seems to be the lowest because it relies heavily on oil, prompting it not to be economically fit. This means that, if no one buys oil anymore, they will experience economic downfall.


Environmental, and Governance

For the indicators, the study group chose the following indicators

Environmental:

Natural resource depletion is the sum of net forest depletion, energy depletion, and mineral depletion. Net forest depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime. It covers coal, crude oil, and natural gas. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.

Social:

Voice and Accountability captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

Governance:

Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.

ESG Data Insights

  1. Adjusted savings: natural resources depletion (% of GNI)

    Philippines does not deplete its resouces too much. With regards to the extremes, Brunei is the most natural resource depleting ASEAN Nation, since they rely heavily in drilling Oil. On the other hand, Singapore is the lowest for they rely on imports, not on their natural resources (or the lack thereof).

  1. Voice and Accountability: Estimate

    Surprisingly, Philippine Citizens are the allowed the most participation when it comes to selecting its government and the citizen's freedom of speech. This is may be due to the fact that it is a democratic country, with people heaviliy influenced by western ideology. As compared to other ASEAN Nations that are comprised of Monarchies.

  2. Government Effectiveness: Estimate

    It was not a surprise that Singapore was the most effective, at least according to the data. Nonetheless, it was comforting to know that Philippines rank somewhere in the middle (even though it lies at the abscissa) when it comes to effective governance among the ASEAN Region.

Overall Government Response to the COVID-19 Pandemic

Actual Visualizations

The visualizations from hereon come in three. The differences in these are the y-limits of each axes. The arrangements of these visualizations follow 3 cases and are stated as follows

Case 1: Uniform Y-Limits for BOTH Cumulative COVID-19 Cases per Thousands and Overall Government Response

Case 2: Uniform Y-Limits for ONLY Overall Government Response

Case 3: Default Y-Limits for both; these were the default scales from matplotlib

The first step was to 'sync' the arrangement of countries of the COVID Dataset list and the Government Response list, since, as stated before, the notebook heavily relies on list comprehension.

Cumulative Cases per Thousands

Case 1:

Uniform Y-Limits for BOTH Cumulative COVID-19 Cases per Thousands and Overall Government Response

Case 2:

Uniform Y-Limits for ONLY Overall Government Response

Case 3:

Default Y-Limits for both; these were the default scales from matplotlib

COVID-19 Insights

With regards to the initial response to the COVID-19 Pandemic, it can be observed in the plots above that all countries responded immediately after COVID-19 cases were detected in their nations, as illustrated by the sudden spikes in the response scores in the early months.

With regards to effectivity however, with respect to the total cases per thousands, the Philippines come second to the last. Singapore has it the worst. This may be a product of the large population density in Singapore and the Philippines despite other countries having more raw COVID-19 cases.

Daily Cases per Thousands

The visualizations and statistics that follow are not necessarily needed. This is because our study group is initially following Oxford's method of super-imposing the government response scores to cumulative cases. The line plots below serve only its purpose for additional insights regarding the number of "spikes" or "waves" of cases experienced by each ASEAN Country; but the graphs below give insight nonetheless.

Actual Visualizations

Case 1:

Uniform Y-Limits for BOTH Daily COVID-19 Cases per Thousands and Overall Government Response

Case 2:

Uniform Y-Limits for ONLY Overall Government Response

Case 3:

Default Y-Limits for both; these were the default scales from matplotlib

Discussion of Results / Visualizations

Economic Fitness

As defined, Economic Fitness (EF) is a measure of a country's ability to produce complex goods on a globally competitve basis. Achieving a high value of EF indicates that a country has enough capabilities to product a wide-range of products, aquire more predictable long-term growth, and secure good competitive position against other countries. Meanwhile, acquiring a low value of EF signifies that the country is most likely to suffer from poverty, with low capabilities and unpredictable growth.
As we refer to the visualization on the Economic Fitness of ASEAN countries, one could perceive that *Brunei, Laos, Cambodia, and Myanmar* have the least measure of EF, which, by definition, indicates that such countries are under extreme poverty and have difficulty with competing globally in producing complex goods. It could be viewed that *Brunei*'s boxplot measure is at an average of 0.011890, with highest EF measure reaching up to only 0.056677, lowest at 0. However, it is likely that the Brunei exhibits an extremely low measure due to its extreme reliance on oil, prompting it to not be economically fit. In a situation where oil is oil isn't as important as it is now, the country is sure to experience economic downfall. *Laos* measures an average EF value of 0.104160, achieving at most 0.180170 throughout the years, a possible effect of the country's underdeveloped financial sector and delayed private-sector growth (Index of Economic Freedom, n.d). *Cambodia* has garnerned an average EF measure of 0.169625 and has ranged from 0.023143 to 0.409337. Although, we must note that the highest EF value attained serves as an outlier to the dataset of Cambodia. Lastly, *Myanmar* has generated a 0.109511 EF average, with minimum and maximum measures ranging from 0.032911 to 0.152874. Furthermore, remaining ASEAN countries such as *Indonesia, Malaysia, Philippines, Singapore, Thailand, and Vietnam* have acquired an average EF of atleast 1.0, with *Thailand* having the highest average EF measure of 2.182506, such a measure was attainable due to the country's GDP growth and continuously expanding economic freedom, as it shifts from a low-income to an upper-income country (Index of Economic Freedom, n.d.). This is followed by *Singapore* whose EF measure ranges from 1.465600 to 2.295572, with an average of 1.772365. Subsequently, *Malaysia*, obtained an EF measure of 1.553873, *Indonesia* with 1.491691, *Vietnam* acquiring a measure of 1.029285, and lastly, *Philippines* with an EF of 1.018214. The aforementioned countries acquiring atleast a 1.0 measure signfies that they are performing better in terms of producing a vast-range of products, with foreseeable growth.
Additionally, the data visualization of the EF measures shows that *Indonesia, Philippines, Cambodia, and Singapore* have outliers in their datasets, indicating that there were instances throughout the years 1995 to 2015 where EF measures may have surged or plummeted, possibly brought about by the country's competitiveness and capabilities.
Ranking all the ASEAN countries based on its average EF measure, *Thailand* would rank first, followed by *Singapore, Malaysira, Indonesia, Vietnam, Philippines, Cambodia, Myanmar, Laos* with *Brunei* ranking last as it has the least EF measure from the years 1995 to 2015. Given that the dataset wasn't able to capture the Economic Fitness measure from five years ago (2016 to 2020), it is highly likely that the EF measures of the mentioned countries, as well as their ranks, are subject to change.

Environmental, Social and Governance

  1. Environmental

    Brunei is one of the countries with the most depleted resources with the country being one of the main resources for oil. This highly affected its rank giving a high score in natural resource depletion. Philippines being rich in resources does not deplete its resources as much.

  2. Social

    With democratic countries such as Philippines, the voices of the citizens are highly valued thus resulting to high social scores as compared to other countries. Freedom is respected in the country which gives the people the power to choose, express, and associate.

  3. Governance

    With effectiveness of the government, Singapore is the leading among the countries. With Singapore’s highly developed economy this does not come as a surprise. Additionally, Philippines as one of the developing countries did not come far behind as it is still on its way to becoming one of the greatest.

COVID-19 Crisis Response

In terms of case 1 for cumulative cases, if one looks closely at the graphs, notice the spike in government response after just a few days of the first case per country. We can infer that each country responded immediately which indicates a positive sign. Whether these were through stay-at-home orders, lockdowns, border closures, and/or other financial government response tools, these were common to most countries in curbing the spread of COVID-19.
Another observation is that compared to other ASEAN countries, Singapore has noticeably peaked in terms of their cumulative cases, and Philippines follows as the second worse. But as we looked at other references, Singapore’s average response in managing the pandemic actually ranks comparatively high. For that reason, we interjected the population density and found out that the reason why Singapore has the highest covid-19 cases among ASEAN nations, despite being normalized to “per thousands”, is because their population density is quite high. In fact, it has the second greatest population density in the world. So because the people are compacted, the virus is most likely to spread easily. Then again that is not to undermine the level of response the government puts in. For Cambodia, it is also possible that its location may have played a role in its success for curbing the spread as the country is surrounded by Vietnam, Laos, and Thailand, all of which controlled the virus relatively well for most of 2020. In the latter part of 2020, however, we can observe that the line increases for Cambodia and other countries which means that there could have been an outbreak of imported cases which called for the increase in government response.
The case 2 for cumulative cases just gives it another perspective for us, but we can see based on the y-limits for cumulative cases how minute their intervals are like Laos, Indonesia, and Vietnam as compared to the other countries. Nevertheless, we dont think that the government should be complacent in responding to these cases. Overall, the level of government response is able to keep up with the cumulative cases–and in some cases, the government had even outdone themselves.
With the population density in mind, the daily cases per thousands was added. For the first case, it is possible that in some countries, the spread may have increased but the government was able to curb the spread right away (e.g. Brunei). If we turn our attention again to Singapore, we can see that there is a sharp increase in the number of cases in May. This could be linked to overseas workers who are required to be quarantined in dormitories. So although we may see a high government response during that peak, it is possible that they may have strayed their attention away from overseas workers and merely focused on the natives. This would also be a similar case for the Philippines in terms of our OFWs because once they are directed to a quarantine facility, a lot of them stay in one room so there is no proper segregtion and social distancing. In short, we infer that there may be a poor response directed to overseas workers which calls for more attention.

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